Networks configurations: In our experiments, we use three popular deep neural networks, ResNet-34, DenseNet-BC (L=100, k=12, droprate=0), and WRN (L=28, widen-factor=2, droprate=0.3) for evaluation.
ResNet-34 and WRN are the classic implementations from their original papers [1]}, [2]}. The settings of DenseNet-BC are following the same setup from Mahalanobishttps://github.com/pokaxpoka/deep_Mahalanobis_detector.
